Execution Monitoring

Execution & Monitoring

This skill covers running and monitoring n8n workflow executions — triggering runs, checking status, debugging failures, and understanding execution data.

Available Tools

Executing Workflows

Manual Execution

Tool: execute_workflow
Input: {
  "workflow_id": "wf_abc123"
}

Execution with Input Data

Tool: execute_workflow
Input: {
  "workflow_id": "wf_abc123",
  "data": {
    "name": "Test User",
    "email": "test@example.com"
  }
}

Listing Executions

All Recent Executions

Tool: list_executions
Input: {
  "limit": 20
}

Filter by Workflow

Tool: list_executions
Input: {
  "workflow_id": "wf_abc123",
  "limit": 10
}

Filter by Status

Tool: list_executions
Input: {
  "status": "error",
  "limit": 10
}

Execution statuses: success, error, waiting, running

Execution Details

Tool: get_execution
Input: {
  "execution_id": "exec_xyz789"
}

Returns:
- Execution status (success/error)
- Start time, end time, duration
- Node-by-node output data
- Error details (if failed)

Common Workflows

Execute and Verify

1. execute_workflow(workflow_id) -> Get execution_id
2. get_execution(execution_id) -> Check status and outputs
3. If error: identify failing node and error message

Debug Failed Execution

1. list_executions(workflow_id, status="error") -> Find failed runs
2. get_execution(execution_id) -> See full error details
3. Identify:
   - Which node failed
   - What error message
   - What input data the node received
4. get_workflow(workflow_id) -> Get workflow to fix
5. Fix the issue (update node parameters, add validation, etc.)
6. update_workflow(workflow_id, fixed_data) -> Save fix
7. execute_workflow(workflow_id) -> Re-test

Monitor Workflow Health

1. list_executions(workflow_id, limit=50) -> Get recent history
2. Count success vs error executions
3. Identify patterns (time of failure, specific nodes)
4. Report findings and suggest improvements

Activation Lifecycle

// Enable trigger-based execution
activate_workflow(workflow_id)

// Disable triggers (manual execution still works)
deactivate_workflow(workflow_id)

Error Types

Retry Failed Executions

Manual Retry

1. list_executions(workflow_id, status="error") → Find failed execution
2. get_execution(execution_id) → Understand the failure
3. If issue is transient (API timeout, rate limit):
   execute_workflow(workflow_id) → Re-run the workflow
4. If issue is in the data:
   execute_workflow(workflow_id, input_data) → Re-run with corrected data

Execution with Input Data

Tool: execute_workflow
Input: {
  "workflow_id": "wf_abc123",
  "data": {
    "key": "value",
    "orderId": 42
  }
}

Best Practices

1. Test before activating — use execute_workflow with test data
2. Check execution details — don't assume success, verify node outputs
3. Monitor error rate — regularly check list_executions(status="error")
4. Use meaningful test data — test with realistic inputs
5. Debug from execution data — node outputs show exactly what went wrong
6. Deactivate flapping workflows — disable workflows that keep failing until fixed
7. Retry transient failures — API timeouts and rate limits often resolve on retry
8. Set up error notifications — configure Error Trigger workflow for critical processes